Detection and Characterization of Cancerous Tumors

ABSTRACT

Processes and techniques are disclosed to identify regions of suspected malignancy and their localization within a body part or organ. These methods rely on the analysis of infrared images to identify thermal abnormalities using image post-processing techniques, numerical modeling, iterative solutions methodology or a digital library. The methods utilize noninvasive, non-radiative and no contact infrared imaging that can be used for breast cancer screening for improved prognosis.

CROSS REFERENCE

This application claims the benefit of the filing date of U.S.Provisional Patent Application Ser. No. 62/684,284, filed Jun. 13, 2018,and this application claims the benefit of the filing date of U.S.Provisional Patent Application Ser. No. 62/727,146, filed Sep. 5, 2018,which are hereby incorporated by reference in its entirety.

This invention was made with government support under grant numberCBET-1640309 awarded by National Science Foundation. The government hascertain rights in this invention.

FIELD

The present disclosure relates to methods for detection andcharacterization of cancerous tumors.

BACKGROUND

Breast cancer (BC) is the most common form of cancer among women in theUS, with more than 246,000 new cases diagnosed in 2018. It is estimatedthat one in every eight women (12.5%) will develop BC during herlifetime and that one in every 24 woman (4.16%) will die of BC. Earlydetection of BC is crucial to increase the survival of individuals;since 2003, the mortality rate has dropped ˜1.2% annually mainly due toimprovements in detection and treatment. Improving BC screeningtechniques for more accurate early detection will save lives. The exactreasons for developing BC have not been determined, but researchersagree that age and breast density are two of the most important factorsthat increase the risk. In the U.S., ˜80% of individuals diagnosed withcancer are at least 50 years old. The risk of developing cancer is sixtimes greater for women with more than 60% dense tissue as compared towomen with ˜30% dense tissue; approximately >40% women have dense breasttissue. There are a variety of screening techniques available to detectBC. However, mammography, the most accepted and widely used, issuboptimal in breasts with dense tissue (found in 40-50% of women).Digital breast tomosynthesis (DBT), similar to mammography, is beingexplored to improve cancer detection, however it suffers from subjectdiscomfort, higher cost and increased radiation exposure. MagneticResonance Imaging (MRI) is an option but is time consuming and tooexpensive to be used for general screening and as such is limited tohigh risk populations (lifetime risk >20%) by most insurers. Ultrasoundis one of the most commonly used adjuncts for the general population;however, it is very operator dependent and findings may be difficult toreproduce.

Infrared Imaging (IRI) has the potential to detect thermal signaturesthat allow for detection of BC with less likelihood of dense breasttissue masking the tumor. IRI captures the heat emitted by the breastsurface and generates a thermal image, which is altered by the presenceof a tumor. Dynamic IRI, with a cold air blast on the breast surface,induces discomfort and is ineffective for deeper tumors. Steady-stateIRI is painless and does not expose subjects to radiation. Since IRIdoes not require special equipment other than the IR camera andpositioning stand, it can be easily incorporated into existingmammography centers. However previous usage of this technique reportedin literature is not founded on rigorous scientific approach.

Various methods to detect and analyze thermal contours include thermaltrajectories, contouring and thermal indices. One study described amethod to determine a 3D thermally distinguishable region. The methodconsists of obtaining a thermal image defined over a 3D spatialrepresentation of a living body. Later, spots are identified in thethermal image and a thermal path is calculated. At least two thermaltrajectories (paths) are used to determine at least one internal 3Dthermally distinguishable region. Another invention created a method fordetecting, diagnosing and guiding treatment of cell irregularities in anexamined tissue. The steps of the method are: applying thermo-modulatingmeans to at least a portion of said examined tissue (cooling orwarming), collecting thermal data of the tissue, and calculating atleast one heat transfer index over time (between 10 ns and 10 s). Thethermal index is computed as the derivative of any order of such thermalindex over time. The thermal index is normalized in a scale between 1and 10, where higher values indicate more severity of the cellirregularities. The index is associated with malignant tumor,precancerous tumors, benign tumors, infections, pneumonia, necroticcells and any combination thereof. The indexes are computed in a pixelor set of pixels with salient thermal characteristics. These techniquesare often simplistic, not reliable due to lack of scientific rigor, orrely on operator experience to identify suspicious tumors.

Other methods explore a combination of techniques in order to adequatelycompare surface features. Some inventors developed a system using bothIR imaging and x-rays. The device is housed inside a closed chamber,where both acquisition systems are used simultaneously. Patients areimaged in prone position without compression. The device is used tocorrelate anatomical and physiological characteristics and post processanalysis in order to reduce the number of false positives. The devicehas multiple IR cameras, one of the cameras is aligned parallel to thex-ray source, other IR camera is aligned perpendicular to the x-raysource, other positions of the IR camera can be used to obtain cranial,medial, candid, lateral and frontal images. The x-ray and the IR camerascan rotate around the opening and can be moved along the x and y axes.Another invention made a system and methods to improve the performanceof breast IR imaging by employing a combination of near IR and mid IRfrequencies for detection of cancer and other subsurface defects. Thesystem also contains an IR transparent window that can be used todistort the breasts or impose an artificial heat flow to and from thebreasts. Near IR provides information about deeper structures because itis a penetrating wavelength and mid IR provide information about surfacecharacteristics. The device uses a source of mid IR wavelength such asled lamps. Some inventors created a similar system that consists ofthermal and electrical impedance scanning together. The novelty of theirinvention is the frequency dependence of the electrical impedance of thetissue, which allows the acquisition of multiple thermal images withcurrents at different frequencies injected in the region underinspection. The method without application of electrical currentprovides conventional thermal images. The application of electricalcurrent enhances the thermal contrast in the surface, depending on theelectrical properties of the tissue. The IR detector must operate in twobands (MWIR and LWIR). The images without electric current and withelectric stimulation are compared at different frequencies. Electrodesare attached to the surface for delivering the electric current. Anotherused a device for multi-modality test of breast cancer utilizingthermography, ultrasound and optical spectroscopy.

SUMMARY

In accordance with one aspect of the present disclosure, there isprovided a method of obtaining infrared images, generating thermalindicators from an infrared image of a body part and identifyingsuspected malignancy.

In accordance with another aspect of the present disclosure, there isprovided a method to localize suspected malignancy within a body part bygenerating a 3D digital model and comparing phantom thermal images withsurface infrared images.

In accordance with another aspect of the present disclosure, there isprovided a method to generate a digital library with geometric andthermal identifiers of various body parts for comparison with clinicalimages.

These and other aspects of the present disclosure will become apparentupon a review of the following detailed description and the claimsappended thereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a camera table setup with turntable in accordancewith an embodiment;

FIG. 2 illustrates an embodiment of an infrared imaging breast imagingtable with camera setup underneath;

FIG. 3A shows a close-up schematic of the front view of the old tablewith the subject lying on one breast, FIG. 3B shows the proposed futuretable with two holes and FIG. 3 C shows the same table with one breastpulled aside with a perforated cloth;

FIG. 4A shows a top view of a clinical bed with two holes and modularpieces to fit to each subject and FIG. 4B shows a bottom view of the bedwith a subject lying on top;

FIGS. 5A-5C show a series of images of the left Breast with Tumor ofPatient 23, FIG. 5A two minutes into acclimation, FIG. 5B middle ofacclimation time—5 minutes in, and FIG. 5C end of acclimation time—9minutes later;

FIG. 6 is a flow chart of Method A to detect malignancy within tissue;

FIG. 7 is a flow chart of Method B to detect malignancy within tissue;

FIG. 8 is a flow chart of Method C to detect malignancy within tissue;

FIG. 9 is a flow chart of Method D to detect malignancy within tissue;

FIG. 10A is an example of a grid having latitudinal and longitudinallines and FIG. 10B is an example of a grid having horizontal andvertical lines;

FIG. 11A is a breast with tumor hot spot and vein hot spot, FIG. 11B isa gridding system on breast, FIG. 11C is a refined mesh highlightingtumor hot spot and FIG. 11D is a refined mesh highlighting vein hotspot;

FIG. 12A is a thermal grid composed of latitudinal and longitudinallines on a breast infrared image and FIG. 12B shows statisticalindicators in selected regions of the grid;

FIG. 13 is a breast infrared image of an individual with breast cancershowing selected lines to report temperature profiles;

FIG. 14A is a temperature profile along the selected Lines 1 and 2, FIG.14B shows smoothed profiles, FIG. 14C is a temperature profile along theselected Lines 3 and 4, and FIG. 14D shows smoothed profiles;

FIG. 15 is an IR image of a breast with tumor showing two regions ofinterest;

FIG. 16 shows temperature contours on a breast thermogram of anindividual with breast cancer;

FIG. 17 is a flow chart of a procedure to generate a digital breastmodel from IRI images;

FIG. 18 illustrates results of geometry reconstruction for a test case;

FIG. 19 shows the computational domain used to compute the temperaturedistribution;

FIG. 20 is a comparison of a series of clinical and computed thermalimages;

FIG. 21 is a schematic of a region to analyze in clinical and computedthermal images;

FIG. 22 is a flow chart of an iterative algorithm to estimate tumorparameters from thermal images;

FIG. 23 is a flow chart of a method to estimate thermal parameters in adigital breast model in a prone position;

FIG. 24 shows a computational domain used in the simulations;

FIG. 25 shows a region of interest within the breast;

FIG. 26 illustrates tumor location and size estimation for differentcases;

FIG. 27 shows a series of volumetric images of breast with actual tumor,estimated tumor and registration;

FIG. 28 shows Axial and Sagittal views of the breast to obtain geometricparameters; and

FIG. 29 is a flow chart of a procedure to compute phantom thermaltemperature distributions from a 3D digital breast model.

DETAILED DESCRIPTION

Thermography is able to detect malignant tumors in tissues in a bodypart, such as the breast, based on temperature variation due toincreased vasculature (angiogenesis) and increased blood perfusion tothe affected area. The terms Thermography, infrared imaging, IR imagingand IRI are used interchangeably to mean images obtained in the infraredfrequency range. The temperature profiles that result from IR imagingshow any temperature variation over the body part surface, includingthose resulting from random vasculature especially near the body partsurface, tumors, hormonal changes, and outside influencing factors suchas alcohol/coffee consumption, clothing changes, makeup anddeodorant/lotion. The present disclosure deals with providing geometricand thermal identifiers, specific markers and techniques to identify thepresence and size/location of malignant tumors from the infrared imagesof the body part. These images are obtained by an infrared (IR) camera.Although the disclosure is described in greater detail using the femalebreasts and breast cancer, the disclosure covers the presence ofsuspected malignancy in any tissue in the body. Suspected malignancyrefers to an area of suspicious cellular activity indicative of cancer.These may be obtained under steady-state or dynamic conditions usingsteady-state or dynamic Infrared Imaging (IRI). This disclosuredescribes various methods for malignancy detection in tissue. Breastcancer is an ideal candidate for IR imaging because the breasts lie inbetween the chest wall and the environment. There is no empty space inthe body for other factors to block or alter the resulting thermalimages. IR imaging could be incredibly beneficial for other diseaseswith a similar ideal setting. Examples of other potential diseasedtissue that can benefit from this technology are listed. ThyroidCancer—Thyroid cancer is rarer in the United States compared to breastcancer and colon cancer. It is also highly treatable with a lowmortality rate. Similar to breast cancer, it is often initiallydiscovered through self-examination. A lump or nodule discovered in thethyroid leads to an ultrasound and biopsy before cancer is diagnosed.Detecting the early signs of thyroid cancer can be challenging. Althoughthe prognosis of thyroid cancer is very good, early detectability isalways an issue. Because the thyroid is located close to the necksurface, surface thermal imaging would be simple. Skin Cancer—Althoughmuch of skin cancer can be visually assessed, rarer forms of skin cancercan be harder to diagnose. Skin cancer involves lesions, bumps,inflammation, or internal disease. Determining the extent of the cancerand later stage metastasis such as lymph node involvement is whereimaging techniques are needed and where IR imaging could particularlycome into play. Testicular Cancer—Testicular cancer is a rarer form ofcancer with only approximately 20,000 cases in the United States peryear. Similar to breast cancer, detection is similar by feeling a lumpin either testicle. Due to the similar properties of the breast andbreast cancer, IRI could easily be implemented to screen for testicularcancer. Non-Cancerous Diseases—The heat and increased blood perfusionassociated with malignant tissue provides a good environment for thermalimaging. Other diseases, such as inflammatory diseases, are also strongcontenders for this diagnosis modality. Diseases such as neuropathy,joint inflammation and arthritis, and various bowel diseases have beenimaged using IR imaging.

Breast cancer-related mortality rates in the United States havedecreased due to advancements in screening and treatment. The prevalenceof breast cancer in the United States is higher than in the developingcountries. However, the mortality to incidence ratio in developingcountries is higher. Although some cancers are easily detected afterprogression and metastasis, the survival rates become much lower. Anincrease in annual breast screening could dramatically improve survivalrates in developing countries. However, the current techniques forbreast cancer screening are expensive, not portable, adversely affectedby the presence of dense tissue and cannot be easily adapted in remotelocations. Thermal imaging can easily be made portable forimplementation in remote or rural setting. With today's updatedtechnology, some of the infrared cameras are compact enough to connectto a smartphone. Potential future application could include thedevelopment of a mobile application for onsite analysis of infraredimages during screening. The only additional needed equipment is a seator stool and an added support system to screen someone in the proneposition with the breasts hanging freely, without gravitationaldeformation often seen in supine or seated positions. Portable infraredimaging camera is used in specific orientations including but notlimited to frontal, oblique views, downward looking, upward looking onthe body part being imaged. A body part refers to any body part or organin both humans and animals that can be clinically imaged. Resultingimages can be transmitted to an image processing center for furtherevaluation. The images can be further processed using the techniquedescribed below, and suspicion is determined. The results can bediscussed with the consulting physician and further evaluation can beprescribed for thermal abnormality identification and specification.

Although prone position is desirable to image the breast, otherpositions used in IR imaging can be used for detecting cancer using thisinvention. The wireless connectivity and ability to transmit the imagesto a central station where the images are used for further analysisusing detection software, including the ones from this disclosure willfurther improve the early detection rate.

Throughout the IR imaging and detection process, there are many keysteps. The patient enters the room and is screened. IRI involves theacclimation of the body part to be imaged in order for steady state tobe reached. Acclimation includes quasi-steady state conditions wheretemperatures are reasonably steady over a sufficient time, including aten to twenty minute duration, five to ten minute duration, or one tofive minute duration. Once the imaging begins, it involves the capturingof from 1-20 images or more around the body part. The images are used toidentify abnormalities associated with malignancy. The images can befurther processed using software techniques and numerical simulationtools to characterize the tumor. Tumor characterization involvesidentifying at least one or at least several of the parameters such astumor size, tumor location, tissue thermal properties, metabolic heatgeneration of the tumor, metabolic heat generation of the non-canceroustissue, fat layer thickness, and fat layer thermal properties. Somefurther characterization involves shape of the tumor.

An example of this method uses the breast and IRI to detect malignancy.The IR imaging table can also be redesigned for other bodily purposes,for medical reasons, for comfort, etc. including but not limited to,adjustments in the center of the table for subjects who are morbidlyobese or pregnant, subjects who cannot be in the specific position,subjects who would rather be kneeling, etc. The specific positionincludes upright, supine, lying sideways, or prone position dependent onthe body part being imaged. In the instance of screening for breastcancer, when a subject enters the infrared imaging room, she isrequested to disrobe from the waist up. Although the subject isgenerally female, the technique can be used for other genders. Thetechnique can be used for other living species including but not limitedto dogs, cats, horses, etc. A hospital gown for privacy is given andworn with an opening in the front. The subject lies down on the table inthe prone position with one breast placed in the opening in the table. Aperiod of, for example, 10 minutes is allowed for proper acclimationfrom the surrounding room temperature. This period may be increased ordecreased depending on the acclimation time needed for the breastsurface to reach near thermal equilibrium state with the surroundings.Prior to imaging, subjects are asked a variety of questions to determineif any external factors will affect the resulting images. They may beasked some or all of the questions, or other questions designed to getthe relevant information related to each question. Additional relevantquestions that affect the thermal profile may also be asked. Forexample, when screening for breast cancer, the following questions arediscussed with the patient:

Have you sunbathed within five days prior to the exam?

Have you used lotion/cream, makeup, deodorant on the breasts on the dayof the exam?

Have you exercised today?

Have you smoked or had alcohol today?

Have you had coffee or tea today?

Were you wearing tight-fitted clothing?

Are you post-menopausal? If not, what is the current day of yourmenstrual cycle? When was your last period? Do you have a regular cycle?

Are you on birth control? If so, what type?

Did you have a previous or a recent injury to your breast?

Did you recently have surgery?

Did you have any surgery on breast?

Do you have a fever?

When steady state is reached, infrared imaging begins. Images are taken,beginning at the head, separated by 45° looking up at 25° angle tovertical. The process of taking each image may only take about 30seconds-1 minute. Shorter or longer duration may be needed depending onthe camera and imaging setup. One example of a camera mount is shown inFIG. 1 but the camera setup can be created in a number of ways. Theresolution of a suitable IR camera that is used is 640×512 pixels, witha thermal sensitivity of 0.02° C. (FLIR SC6700). Other cameras thatprovide the necessary thermal information may be used with differentpixel sizes and resolution. The angle and focus of the camera variesbased on the tissue being screened. Other angles to vertical orientationand angular separation may be used to obtain specific or detailed imagesof the body part.

In order to obtain images of potentially suspicious tissue, an imagingtable is used to facilitate screening. This table can be designed in amultitude of ways dependent on the tissue being imaged and can bemodular to fit many body types or abnormalities. In the example ofimaging breast cancer, using imaging positions such as the supine andupright positions, imaging the breasts with infrared imaging can causeunwanted thermal distortions in the inframammary fold, between thebreast and the underside of the breasts. Examples of some table designsused to screen the female breast for breast cancer are discussedfurther. One such embodiment, seen in FIG. 2, involves a table with acircular opening where the breasts are exposed to an IR camera mountedon a stand that moves on a circular track. The purpose of the table isto facilitate obtaining infrared images in the desired view of thebreast. The imaging table is a retrofitted lab table with a 9-inch holefor the breast imaging. A 2-inch layer of foam is placed on top forcomfort and a layer of disposable paper is used for cleanliness. A blackcurtain was added around the edge of the table to hide the cameraequipment and ensure no reflection or stray thermal artifacts duringimaging. Additional acclimation time may be given for the acclimation ofthe contralateral breast because the subject was lying on that breast onthe table during the imaging of the first breast. This causes the breastto warm and takes longer for steady state to be reached. A change in thedesign of the imaging table is presented to avoid this and ensure ashorter acclimation period by allowing both breasts to hang with aseparating fabric that is used to obtain clear view of the breast beingimaged.

In addition, the following techniques are disclosed to reduce theacclimation time. It is imperative with thermal imaging to observe theentirety of the breast surface and reduce any unwanted thermalalterations caused by the breasts touching each other or the breasttouching the chest wall. The subject is imaged in the prone position,similar to an MRI. The original table design was meant to replicatetables used for stereotactic breast biopsy with one opening in thecenter to observe one breast at a time. However, the current table usedfor clinical imaging causes potential unwanted thermal alterationsduring imaging. The acclimation time needed to ensure steady statecondition suitable for infrared imaging varies from 1 minute to twentyminutes. In some cases, a longer duration may be implemented. In anembodiment, all testing takes approximately 23 minutes. Changes in theprotocols, imaging duration and intervals between images will causechanges in the total imaging time. The subject enters the room, disrobesfrom the waist up and places their right breast into the opening in thetable while the left breast is tucked against the table. Ten minutespass for proper acclimation and to ensure that the temperature on thebreast reaches steady state. When the subject switches sides, thecontralateral breast is significantly hotter. This requires a longeracclimation period to ensure that the temperature of the contralateralbreast reaches steady state before imaging continues. Another embodimentof a clinical table for breast cancer screening is closer in design to abreast MRI table. Two holes are added, one for each breast, with aperforated cloth or a flexible fabric lying between them. The cloth isadded to move one breast out of the way so the other breast can beimaged in a 360° view. In FIG. 3, the first panel shows the squishedbreast lying underneath the patient while the contralateral breast hangsfreely through the opening in the table. This is found in the currentclinical design, causing a longer acclimation and imaging period. Thesecond panel shows the side view of a redesign with two holes cut in thetable and the added cloth in between the two. The final panel shows thecloth is swept to the side to move the second breast out of the way.Although one breast will be touching a cloth, the perforations willprevent the breast from heating and will reduce acclimation timeconsiderably.

In addition to the added perforated cloth in between the two breasts,adjustable holes are added to fit to the patient. The table and theimaging system are designed to accommodate different ranges in breastsize, shoulder width, body contour, and weight. In order to have onetable that matches all patients, it can be made adjustable. A top viewand bottom view of the new table with modular design is seen in FIG. 4.

There are modular pieces in the horizontal and vertical directions foreach breast hole. When the subject lies on the table, the modular pieceswill be adjusted to fit their breast size and weight. Once everything isadjusted accordingly, the perforated cloth will pull one breast to theside (FIG. 3C) and the acclimation period will begin. Extra padding willbe added between the two modular holes to ensure subject comfort. Theperforated cloth serves the function of pulling the non-imaged breastaway so that clear access can be achieved for the IR camera. It needs tomove the breast without causing significant thermal changes due toinsulating effect. The perforations serve the purpose of providing aircooling while the breast is moved away. The perforated cloth may bereplaced with a mesh, be made of different materials including wire,polyester, nylon, etc. Other potential modular designs could include aspecially made gown for each patient with holes where the breasts lieand a larger slot within the imaging table. This would significantlyreduce the influence of the chest wall on the resulting thermal imagesand would provide more comfort for the patient. Instead of altering thegown, a softer, cloth-made modular design could be implemented for thesame purpose. Altering the clinical imaging table as opposed to thesubject gowns will provide much more control and help with cleanliness.

Acclimation Time—The desired acclimation time to reach steady-state isbetween 5 minutes and 20 minutes for efficient throughput with goodimaging quality for accurate detection. A faster acclimation time may bereached by providing loosely fitting clothing or minimal clothing duringwait period. The time used for the preliminary study was 10 minutes. Theseries of images in FIG. 5 shows the left breast, the breast without atumor, of Patient 23. This patient had a lot of random vascularthroughout the breast, some more striking or larger than other areas.The larger visible blood vessels running through the breast do not havea drastic temperature difference from the beginning of acclimation tothe end. However, the smaller, deeper blood vessels do change over timeand reflect differently on the surface. The change in acclimation timecan change the visible thermal profiles on the breast surface,particularly where the blood vessels and tumor(s) are resting. Onepotential method of creating a more uniform temperature across thesurface is by reducing the heat transfer rate from the breast to theenvironment. An insulating brassiere can be used to sit on the breastsfor a certain amount of time to ensure even temperature is reached. Whenthe bra is removed, the vasculature thermal profiles will change whenexposed to ambient temperature. The resulting changes in vasculaturetemperature can be observed over a period of time. The observablecooling period for the breast once exposed to the ambient temperaturecan help in differentiating hot spots due to tumors vs. hot spots due tovasculature. Hot spots are also referred to as an area of increasedhyperthermia. As the breast cools, the lines of vasculature become moredefined and linear while the thermal regions induced by the tumor remainmore diffuse. Some changes are expected when the tumor is closer to theskin surface and the intensity of the thermal changes is considered.

After the screening process, whether screening breast tissue or otherbody parts, one or more of the images are analyzed and it is determinedwhether or not a tumor is potentially present. The regions of interestare identified where thermal profiles are indicative of abnormalitiesthat may be associated with the presence of cancer. The terms cancer andmalignancy are used interchangeably and cover all forms of canceroustissues. Different methods used to analyze thermal regions of interestare presented and discussed in greater detail in Method A. Method Bdescribes the generation of a digital model of the body part usingclinical images. A digital model is a 3D computer representation of thebody part. The process can generate a 3D digital model of a body partwhether the body part has a single tumor, multiple tumors or isconsidered healthy (free of suspicion). This model can be used togenerate surface and internal temperature distributions of tissues usingthermal simulation software (for example ANSYS Fluent). The generatedtemperature distributions can be compared with the temperaturedistributions in surface infrared images for the same orientation. Themethod also describes criteria that can be used to decide a matchbetween the generated phantom thermal images and surface infrared imageswith actual temperature distributions. The thermal matching criteria aregenerated using Method A and are described further in Method C. Method Cdescribes a technique to identify tumor characteristics including size,location, metabolic heat generation rate, and thermal properties ofdifferent components of the body part including, but not limited totissues and fat layer. Method C can be used before or after suspectedmalignancy is detected.

Method C employs the use of mathematical characterization of thetemperature distributions, comparison of thermal images from thesimulation and from the infrared images and a method to determine someor all of the thermal characteristics of tumor and healthy tissue.

Finally, images can be added to a digital library based on identifiedthermal and geometric identifiers to be used in further analysis anddiagnosis, discussed in Method D. Similar procedures can be adapted forcancers of other body parts. If no regions of interest are identified,the patient may be referred back to a consulting radiologist or primarydoctor. More details on the components of this patient workflow and theunderlying mechanisms are described in greater detail below.

METHOD A: A method for determining malignancy within tissue usinginfrared imaging. —A region of interest is identified as a region on thebody part surface where further analysis is considered based on thethermal abnormalities observed in surface infrared images. Tumors withintissue will have a greater amount of heat propagation throughout thetissue whereas vasculature or other thermally altering features willhave a more defined thermal abnormality such as narrow and longer linesof increased temperature. After an image is taken, a geometric map iscreated on the surface infrared image using various techniques. Thereare multiple techniques that are used to develop a geometric map inorder to detect regions of interest within tissue. These methods involvegridlines, profile lines, thermal contours, visual correlations andstatistical indicators. Other mathematical or statistical parameters canbe employed. Using the various techniques, thermal indicators aregenerated based on criteria specific to the technique used. Regions ofinterest are identified based on the thermal indicators and analyzed forsuspected malignancy. Care is taken to avoid thermal saturation in theimage over the tissue so that accurate information on temperatureprofile and its variation is obtained. Surface infrared images may ormay not represent the exact surface temperature due to emissivitycorrection needed in the image. However, assuming the surface emissivityis uniform, the temperatures indicated by the infrared image arerepresentative of the temperature field which may be somewhat offsetfrom the true value. Use of lotion and other creams, etc. may causechanges in emissivity and their use is discouraged prior to imaging. Thefollowing techniques are used for analysis.

Statistical Indicators—There are several statistical indicators proposedin the techniques discussed below including but not limited to meantemperatures, minimum temperatures, maximum temperatures, standarddeviation, variance, median, etc. in order to determine suspectedmalignancy. Qualitative and quantitative measures are derived based onthe surface infrared image of the body part.

Correlate with Visual—Mastectomies, lumpectomies and other forms of scartissue may create variations in thermal images or create confusingdistortion. Combining IR imaging with visual data could help indetermining if various thermal distortions should be examined moreclosely. However, obtaining visual images can be a sensitive issue fromprivacy considerations. Instead of digital photography, the tissuesurface may be digitally reproduced using MRI images. Digitalreproduction of a body part can be accomplished through other imagingmodalities, including but not limited to, infrared imaging, outlinecapture techniques, shadow techniques, etc. In one embodiment, theimaging operator could write down observations about the visual scars,abnormalities, imperfects, etc. that correlate with factors seen incollected infrared images.

Temperature Distribution in a Thermal Grid—One such system of analysiswould be the implementation of a grid along the body part. This gridcould be sketched in multiple ways including a longitude-latitude typepattern with the longitudinal and latitudinal lines matching thecontours of the body part. Other gridding systems could involve biasedlines, changing line density based on features of focus or an alternatestyle of grid pattern. Although a few types of grids are described, anygrid pattern that can be generated on the infrared image for furtheranalysis or comparison will provide the needed information. The goal ofimplementing a grid is to enable defining the temperature variation ineach segment of the body part. With an overall estimated average surfacetemperature, finding the variation in the mean temperature in eachsection of the body part can make it much easier to find abnormalities.Using a biased grid or a varying density grid can help for variouspurposes. It can also be used to single out various thermalabnormalities. Thermal abnormalities are defined as hot spots or regionsof interest including but not limited to suspected malignancy, suspectedbenign masses, suspected vasculature or suspected scar tissue. Withfewer grid boxes, it will be easier to find the boundaries of thethermal abnormality in order to determine if malignancy is a concern orif stray vasculature is creating additional heat signatures. The gridtemperatures may be obtained as an average of one or more pixels atknown locations in a grid that is mapped over the infrared image of theskin surface. Additional features such as elimination of outliers orsimilar procedures used in image processing may be applied.

Temperature profiles—Another method to identify thermal features is toobtain the surface temperature profile along lines drawn on the skinsurface. These lines can be the same as described for the generation ofthe thermal grid. The temperature profile along these lines can betreated without processing, or processed to mitigate small temperaturevariations. Such processing can be done through Median, Average,Gaussian, Moving Average, Savitzky-Golay, Regression, or any combinationof these filters. If the sign of the temperature gradient along thedistance from the chest wall changes from positive to negative, it maybe used as a marker of the presence of cancer. The distance over whichthe gradient is obtained is an important consideration. If the slopechanges in certain region, while it may not change the sign, it may alsobe indicative of the cancer. If the slope changes by more than 10percent over the certain distance, it may be used as a threshold. Inother cases, a change from 10-50 percent or higher may be used as amarker. The slope is calculated over a reasonable distance to avoid anyimage aberrations. Thermal abnormalities can be classified as atemperature difference between maximum and minimum temperatures higherthan 0.2° C. within a range of 2 mm to 10 mm, a preferred range of 2 mmto 5 mm. Thermal abnormalities are classified as a tumor if thetemperature difference between maximum and minimum temperatures ishigher than 0.5° C. over a 5 mm to 40 mm range, a preferred range of 10mm to 30 mm. Thermal abnormalities are classified as veins if thetemperature difference between maximum and minimum temperatures isbetween 0.2° C. and 2.0° C. over a 1 mm to 5 mm. If an abnormality isclassified as both a vein and a tumor, it is classified as a tumor.

Aspect Ratios—In order to distinguish between possible tumors and bloodvessels in a surface infrared image, the aspect ratio of the region ofinterest can be used. Tumors have a larger area of diffusion from thecenter of the hot spot to the surrounding tissue. Other factors such asveins have a more secluded, well-defined hot spot. By studying variouslengths of hot spots on the skin surface, an aspect ratio can becalculated. This ratio, depending on the resulting value, will helpindicate if an area is suspicious of malignancy or denotes a differentthermal feature such as vasculature.

Thermal Contours—Using thermal contours with the hot spot in question asthe central point is another method to hot spot differentiation anddetection. Similar to a topographical map, thermal contours can be drawnaround the hot spot and the diffusion through the tissue measured.Because of the increase in blood perfusion, malignant tumors have astronger heat presence that steadily warms the surrounding areas,dissimilar to vasculature. Using appropriate markers, it is possible toidentify the thermal changes due to angiogenesis. This may be a resultof observing specific vasculature patterns that are noted inangiogenesis that are different from the regular vasculature. Acomparison may also be made with infrared images obtained from priorvisit or visits of the same subject to observe the thermal artifacts.

METHOD B: A method to generate a digital model and computer simulatedtemperature profiles on the skin surface of the body part from clinicalimages—A digital model is a digital entity that has the actual shape ofthe body part under analysis and can be manipulated and modified. Also,if necessary, a volumetric or surface mesh can be generated on thedigital model. The digital model can be generated from any imaging orvideo modality of the body part under analysis, including but notlimited to digital photographs, infrared images, magnetic resonanceimages, magnetic resonance angiograms, ultrasound images, mammogramseither 2D or 3D, computed tomography scans, data from 3D scanners, laserscanners, depth sensors such as the Microsoft Kinect, video processing,or any combination of these and other imaging modalities. A tumor ofknown characteristics, including but not limited to size, shape,metabolic heat generation rate, and thermal properties of differenttissue or fat can be introduced within the digital model. The digitalmodel can be used to conduct computer simulations to compute temperaturedistributions or profiles for various tumor location and sizes. Theresulting model, referred to as a phantom thermal model, is used forcomparison with the surface infrared image. Appropriate thermal boundaryconditions are employed in the thermal simulation, such as constantchest wall temperature, given heat transfer coefficient or coefficientson the skin surface, ambient temperature, and emissivity of the skinsurface and temperature of surroundings if radiation effects are beingconsidered.

The digital model can be generated from the data obtained from theimaging modalities or by processing one or multiple individual imagesusing techniques such as image filtering, edge detection, segmentation,intensity transformation, multiview reconstruction, photogrammetry,marching cubes, marching tetrahedrons or any combination of thesemethods, or any other method that results in a 3D representation of thebody part. If desired, the digital model can include the internalstructures such as blood vessels and skin and fat layers. The resultingmodel can be used in its current state or modified to remove or addtexture features either using a Computer Aided Design (CAD) software, amodeling software, or any software in which the model can me modified orsmoothed to include new features. The digital model can include one ormultiple tumors with characteristics including size, shape, location,metabolic activity, thermal conductivity, perfusion rate, etc.

Once the digital model contains the desired features, a mesh, eithersurface or volumetric, is generated in order to create smaller regionsto solve the governing equations of heat transfer in the domain. Themesh can be generated by any software or procedure known in the art. Thegoverning heat transfer equations can be solved using availablecommercial thermal simulation software or open source software.Preferably, the minimum number of mesh elements is 1,000 for volumetricmeshes and 100 for surface meshes. A higher resolution can be achievedusing at least 100,000 elements for volumetric and 5,000 for surfacemeshes. Higher or lower number of elements can be implemented dependingon the size of the region and desired overall computation speed oraccuracy. The quality of the elements in the resulting mesh should bewithin recommended values for the software for accurate numericalcomputations. For example, the skewness of the mesh elements should bebelow 0.95, with preferred values below 0.7. Depending on thesophistication of the software used, the actual number of mesh elementscan be smaller or larger. Once the mesh is generated in the digitalmodel, the governing equations can be defined. The governing equationscan be analytical, empirical, semi-empirical or any combination thereof.Some examples are the Pennes Bioheat Equation, the Countercurrent, andthe Jiji models. These governing equations may or may not take intoaccount the effect of the vasculature on the temperature calculations.This effect can be included either using models for the vasculature,from clinical images or artificially generated using software such asVascusynth. Appropriate values of the tissue properties and parametersshould be defined prior to conducting the simulations, some of theparameters include thermal conductivity, specific heat, density, bloodperfusion rate, metabolic activity, etc. The overall goal of the digitalmodel and the thermal simulation is to provide an accurate estimation oftemperature profile on the surface of interest for a given digital modelwith given tumor characteristics.

In order to solve the governing equations in the domain, either for asteady-state or transient formulations, boundary conditions are used todefine the interactions of the computational domain with itsenvironment. The surface is generally exposed to the still air, forwhich a convective boundary condition can be used. The value of the heattransfer coefficient considering a mixture of radiation, naturalconvection and evaporation from the skin is preferably in the range of5-25 W/m²-K; however any value can be used. Other alternatives includeforced convection or natural convection by modeling the surroundings, afixed initial surface temperature, radiation effects or any combinationof these to account for the heat transfer between the model and itssurroundings. For the other surfaces of the domain, any relevantboundary condition can be used, for example, fixed temperature, knownheat flux, known temperature distribution, temperature distribution fromexperimental or analytical data, symmetry, insulated faces. Theseconditions can be either stationary or time dependent.

Once the digital model is meshed and the boundary conditions and tissueproperties are set, the temperature field in the computational domaincan be obtained from the thermal simulation software. The governingequation can be discretized in the software using the Finite VolumeMethod, the Finite Element Method, Finite Differences, The BoundaryElement Method or any other suitable discretization method. The solutioncan be obtained using commercial software, open-access software, bydeveloping in-house scripts/programs/algorithms, or any combinations ofthese. This software can run in parallel or serial mode either on a CPUor GPU (graphics processing unit). The solution can be obtained byrunning the routines in parallel, serial, multithread or single-threadprocesses in any architecture or processor, including, but not limitedto CPU and GPU (graphics processing unit). Any other technique to obtainthe thermal profile for a given digital model can be implemented.

The tumor introduced in the digital model can have any shape, includingbut not limited to spheres, cubes, ellipsoids, cylinders, pyramids,prisms, irregular shapes, actual tumor shape from imaging and anycombination of those. The thermophysical properties of the tissues suchas thermal conductivity, specific heat, blood perfusion, metabolicactivity, or any other, can be constant, variable in space, variable intime, or any combination of these. The variation can be defined by anycontinuous, discontinuous or piecewise mathematical function orcombination of functions. An optional but very highly recommended stepis to validate the temperature computations of the digital model. Inorder to compute accurate temperature distributions, the predictionsfrom the thermal simulation software should match closely to thetemperature observed from the phantom thermal images to the surfaceinfrared images. Therefore, it may be necessary to validate the digitalmodel. Method B can be used to validate the digital model using a casewhere the tumor characteristics are known and the infrared images areavailable. The process steps in one such embodiment may be asfollows—Obtain a digital model of the body part of interest from theinfrared images or other imaging techniques. If available, incorporatethe tumor characteristics in the digital model, generate simulatedtemperature distributions using thermal simulation software, compare thesimulated and actual IR temperature distributions and determine whetherthere is a good match using thermal matching criteria.

In one embodiment, the phantom thermal images are generated using thethermal simulation software saved in views/orientations that reproducethe views/orientations that were used to capture the infrared imagesduring clinical testing. To facilitate the comparison, the phantomthermal images and surface infrared images should correspond to the sameview, orientation and angle of the body part. Image alignment also knownas image registration or registration is an important step whilecomparing the phantom thermal image and surface infrared imaging inorder to assure that the corresponding regions of interest are closelymatched with each other.

The surface infrared images can be analyzed, either by technicians,clinicians, or through software either automated, semi-automated ormanual, to inspect them for abnormal features, including but not limitedto, abnormal lumps, scars, missing tissue and deformation. The outcomefrom the analysis can be used while comparing the computed and infraredimages.

The validation can be conducted by comparing specific parameters in thecomputed and clinical thermal images at the pixel level (2D images),voxel level (3D images), or analyzing portions of the image or images,or the entire image or 3D model. The same or similar procedure can beused to compare images in other scenarios, such as comparing the resultsin an iterative procedure, outlined in Method C. The comparison is usedto validate the digital breast model. It can also be used to verify thematch between computed and infrared images, and update the values of theparameters in the iterative process.

In one embodiment, the portion or regions of the images to analyze canbe a gridding system along the body part. The grid can be sketched aslongitudinal and latitudinal lines following the contours of the bodypart, horizontal and vertical lines, skewed lines, concentric andeccentric lines and any other gridding system to divide the part underanalysis. The grid lines can have a constant spacing or variablespacing. The region to be analyzed can be further divided intosubdivisions, preferably more than 4 subdivisions. A more preferrednumber of subdivisions is more than 16.

In one embodiment, a central region representing between 1% and 100% ofthe image is used for comparison. In a preferred embodiment, a rangebetween 5% and 75% is used, whereas, in a more preferred embodiment, arange between 25% and 50% is used. One of the factors in determining therange is the size of the region of interest, while other factorsincluding, but not limited to, are size of image, registration matchbetween the images.

These specific parameters can be individual indicators described inMethod A, and including but not limited to mean, median, variance,standard deviation, texture, entropy, maximum, minimum, moment,correlation, or any other first or second order statistical parameter ormathematical function of them. The parameters can also be distributionsof values along specific paths such as the gridding system proposed inthis invention, along regions of interest in the images or 3D models, ora combination of these. It is preferred to conduct the comparisonbetween parameters after the 3D models or individual images arealigned/registered to ensure that the comparison is being conductedbetween corresponding regions/levels, any suitable registration methodsuch as intensity-based, feature-based or a combination of them can beused, although no image registration is required. During registration,images may or may not be scaled to the same size, although scaling ispreferred to facilitate the comparison. The comparison of parameters canbe conducted by computing the difference, absolute error, averagederror, mean squared error, correlation, any mathematical functionbetween these or their combinations. The difference between the computedand clinical images in the thermal parameters that provide arepresentative temperature are called as the convergence criteria andshould be below 3° C. for accurate tumor detection, but preferably below1° C., or more preferably below 0.5° C., and most preferably below 0.2°C. The accepted value is balanced between the competing needs to reducefalse positives and improve accurate detection. The convergence criteriacould be based on other indicators such as temperature gradients, wheredifferences between computed and actual should be below 3° C./cm, valuesbelow 0.5° C./cm are more preferred. Any other statistical parameter canbe used to define the convergence criteria either thermal or in terms ofpixel intensities of individual pixels or clusters of pixels from theimages.

METHOD C: A method to localize a tumor within tissue—The digital modelcan be used in software to localize tumor in terms of estimatingrelevant parameters such as thermal conductivity of tissues (skin, fat,gland, muscle, tumor, etc.), blood perfusion and metabolic activity ofthe tissues, location, size and shape of a tumor, or external conditionssuch as ambient temperature, heat transfer coefficient, or any otherrelevant parameter. First, initial values of the parameters required inthe digital model are set using thermal simulation software along withappropriate boundary conditions to generate phantom thermal images. Thedetails of the digital model generation and thermal simulation describedin Method B can be employed in Method C. The surface infrared images arethe target images to which the generated phantom thermal images arecompared. The phantom thermal images and surface infrared images areprocessed and compared using any criteria for comparison, such asdescribed in Method B. If the difference of parameter values between thephantom thermal images and the surface infrared images is below aconvergence criteria determined by the user, the values of theparameters are accepted as the estimates from the software. If thedifference is above the convergence criteria, the parameters are updatedand new phantom thermal images are generated, the process is repeateduntil the convergence criteria are satisfied and the parameter valuesare accepted. The comparison between the phantom thermal images and thesurface infrared images can be done using any of the techniques andalgorithms described in Method B. In order to update the values of theparameters to estimate, any optimization procedure can be used such asthe Levenberg-Marquardt algorithm, The Gradient Descent method, TheConjugate Gradient Method, The Simulated Annealing Method, ParticleSwarm Optimization, Ant Colony Optimization, Sequential QuadraticProgramming, Artificial Neural Networks, Support Vector Machines,Genetic Algorithms, any combination of them and any other existing ornew method suitable to solve optimization problems.

The localization of an area of suspected malignancy is defined asobtaining the location, size and other characteristics of a tumor. Thelocation of the tumor can be measured in terms of a set of coordinates(x, y, z) from an origin to its center of gravity or to any other pointinside the tumor outline or on its surface. Any coordinate system suchas Cartesian, Cylindrical, Spherical, or any mapping and combinationthereof can be used. Any combination and number of parameters can beestimated using the methods described herein. The estimation can beobtained from the phantom thermal images by comparing the thermalidentifiers to a library of thermal identifiers. Other methods toconduct the estimation include Artificial Intelligence algorithmstrained with data in the library of thermal identifiers including butnot limited to Artificial Neural Networks, Support Vector Machines,Convolutional Neural Networks, Genetic Algorithms and any combination ofthese. Another method includes using a digital model prepared from anyof the imaging modalities described herein.

The origin of the coordinate system to locate the tumor can be definedas any point either internal, on the surface, or external to any part ofthe body. The procedure can also be used to locate multiple tumors. Theprocess for multiple tumor identification can be invoked when a singletumor has been identified and there is at least one region for which theclinical and computed temperature temperatures differ by more than 0.3°C., preferred values are above 1° C., more preferred values are above1.5° C. The convergence criteria could be based on other indicators suchas temperature gradients, where regions with differences above 0.5°C./cm are identified, values above 2.5° C./cm are more preferred. Higheror lower values of any of the convergence criteria could be employed toimprove the detection accuracy. Any statistical indicator can be used todefine criteria for multiple tumors, such as thermal indicators orindicators in terms of pixel intensities of individual pixels orclusters of pixels from the images. Thermal indicators refer totemperature changes on the body part surface observed in surfaceinfrared images or phantom thermal images. Other suitable values may beused based on accuracy or speed of simulation, although accuracy is ofprimary concern. The location of the identified tumor is fixed and theprocedure is repeated until a convergence criteria is met for the secondtumor location and size. In case of more regions of discrepancy, theprocedure can be repeated as many times as needed. If multiple tumorsare present, an iterative procedure can be further implemented bykeeping the second tumor fixed and refining the first tumor. Similarstrategy can be used for multiple tumors. The process may be iterativelyrepeated to improve accuracy.

The estimated convergence criteria and parameters can be refined bycomparing the outcome with clinical data, including surface infraredimages. The parameters that can be refined include but are not limitedto, tumor shape, aspect ratio, size, location, and other tumorcharacteristics to include early stages of cancer such as cell liningsoccurring in ductal carcinoma in situ.

One optional but highly recommended step is to analyze the outcomevalues of the estimated parameters, preferably those referring to thelocation and size of the tumor. The analysis can be done by trainedpersonnel such as clinicians, for example the examining radiologist, orsoftware, either automated, manual or semi-automated. The analysis candistinguish between different scenarios, including but not limited totumor size and locations falling within common ranges for the specificcancer under analysis, tumor is not found in the domain under analysisor its location falls outside the domain, tumor is too small and itslocation is beyond common values, tumor is too big, etc. For each of thepossible scenarios, the entity analyzing the outcome will providerecommendations for further analysis. The Method C is both applicable tosteady state or dynamic infrared imaging.

METHOD D: A method to utilize a digital library for comparison—A digitallibrary is generated from images such as clinical images, phantomthermal images, or surface infrared images of any body part in order tostore relevant thermal and geometric data for future comparison withinfrared imaging screening. The library is where information or data isstored in electronic or other media forms. When images of a body partare added to the digital library, geometric and thermal identifiers areidentified. The geometric identifiers of images of a body part arecompared with the geometric identifiers of images found in the digitallibrary. After a geometrically similar match is identified, the thermalidentifiers are compared. The digital library may contain geometricidentifiers, thermal identifiers, and infrared images from differentorientations and distances, patient details, tumor identifiers includingsize, shape and histology, details regarding how the information wasobtained, computer generated thermal images and their geometricidentifiers, thermal identifiers and tumor identifiers. The geometricidentifiers are identifiers that are related to the geometrical detailsof the body part. These libraries can be dynamic and trained withindividual case studies. The tumor identifier may include informationregarding whether a tumor is present or not, its size, shape, type oftumor and other tumor location details that enable location of the tumorwithin tissue. The thermal identifiers may include information on thetumor properties and how they affect the surface temperature profile.They may include information on maximum temperatures, minimumtemperatures and the gradient throughout the tissue.

In accordance with an aspect of the present disclosure, there isprovided a breast cancer detection process using infrared images inwhich geometric and thermal identifiers are generated and compared withidentifiers stored in a digital library to determine tumorcharacteristics.

In an embodiment, a method for breast cancer detection includes:

-   -   a. isolating the breast and obtaining thermal images using an        infrared camera;    -   b. comparing images to those in the digital library for        geometrical identifiers including,        -   looking for similarities in geometry, including but not            limited to breast shape, breast size, breast circumference,            distance from chest wall to nipple, volume, etc;    -   c. comparing images to geometrically similar images in digital        library for thermal identifiers including,        -   utilizing thermal identification methods including but not            limited to thermal contours, thermal profiles, statistical            indicators, and gridlines;    -   d. using thermal indicators to determine abnormality; and    -   e. adding thermal images to digital library for future        comparison.

In accordance with an aspect of the present disclosure, there isprovided a procedure to obtain clinical infrared images of the isolatedbreasts from multiple positions including:

-   -   a. subjects either with or without breast cancer are recruited;    -   b. subjects asked a series of questions about potential        activities and factors that can influence thermal distribution        of the breast;    -   c. one breast is isolated and allowed to acclimate to room        temperature;    -   d. multiple images are taken around the circumference of the        breast; and    -   e. the process is repeated for the contralateral breast.

In accordance with an aspect of the present disclosure, there isprovided a procedure to generate geometric identifiers of the breastthat contain relevant geometric information to define the shape,structure and topology of the breasts:

-   -   a. isolating the breast and obtaining clinical images of        multiple views including,        -   clinical images obtained using methods including but not            limited to infrared imaging, MRI, mammogram and x-ray;    -   b. generating a 3D digital model using collected images;    -   c. measuring multiple factors on the breast including but not        limited to the circumference around the breast at multiple        locations, the distance from the nipple to the chest wall, the        size of the breast, the shape of the breast, horizontal and        vertical dimensions of breast, etc.; and    -   d. adding images to digital library for future comparison.

In accordance with an aspect of the present disclosure, there isprovided a procedure to generate thermal identifiers to identify regionsof increased hyperthermia obtained from surface temperature informationof a body part:

-   -   a. multiple methods are used during post-processing to identify        regions of interest and differentiate between vasculature and        tumors, including but not limited to,        -   i. gridlines including,            -   1. gridlines including latitude and longitude drawn on                the body part creating individual boxed regions on the                body part,            -   2. average temperature in each box calculated,            -   3. temperatures differences between adjacent boxes                greater than 0.3° C. are considered suspicious,            -   4. additional lines drawn closer together in areas of                increased hyperthermia to narrow region of interest,            -   5. tumors or veins identified using increased areas of                hyperthermia also known as hot spots, and            -   6. veins and tumors differentiated,        -   ii. profile lines including,            -   1. lines drawn through regions of identified interest                also known as regions of increased hyperthermia,            -   2. the plots created correspond to temperature changes                over distance along the profile lines,            -   3. slope of the resulting plot lines measured and                identified as abnormalities or normal thermal changes,            -   4. thermal abnormalities are classified as a temperature                difference between maximum and minimum temperatures                higher than 0.2° C. within a range of 2 mm to 10 mm, a                preferred range of 2 mm to 5 mm including,                -   i. thermal abnormalities are classified as a tumor                    if the temperature difference between maximum and                    minimum temperatures is higher than 0.5° C. over a 5                    mm to 40 mm range, a preferred range of 10 mm to 30                    mm,                -   ii. thermal abnormalities are classified as veins if                    the temperature difference between maximum and                    minimum temperatures is between 0.2° C. and 2.0° C.                    over a 1 mm to 5 mm,                -   iii. if an abnormality is classified as both a vein                    and a tumor, it is classified as a tumor,        -   iii. contours including,            -   1. contours drawn around visible hot spots or                temperature differences,            -   2. heightened temperatures detected and identified as                abnormalities,            -   3. aspect ratios used to define regions of interest and                differentiate veins and tumors; and    -   b. abnormalities detected by gridding systems and profile lines        are classified as thermal identifiers and added to digital        library for future comparison.

In accordance with an aspect of the present disclosure, there isprovided a breast cancer detection process using infrared images andthermal images generated through numerical simulations in which amatching algorithm is used to determine the tumor characteristics:

-   -   a. isolating the breast and obtain thermal images using an        infrared camera;    -   b. storing the individual images with image identifiers,        including patient data, orientation of the camera and patient,        geometrical identifiers;    -   c. generating digital breast model;    -   d. generating thermal images of the breast surface including,        -   i. the digital model and initial tumor characteristics,        -   ii. thermal simulation software. Alternatively, artificial            intelligence algorithms including but not limited to neural            networks and support vector machines can be used to generate            the surface the temperature distributions; and    -   e. using matching algorithm to determine tumor characteristics.

In accordance with an aspect of the present disclosure, there isprovided a procedure to generate a digital breast model from clinical oroptical images of the breasts:

-   -   a. a process to generate digital breast models from images        including, but not limited to digital photographs, infrared        images, MRI images, ultrasound images, Mammograms, either 2D or        3D, computed tomography scans, 3D scanners, laser scanners,        depth sensors such as the Microsoft Kinect or any other, any        other imaging modality or video capture from which the breast        outline can be obtained;    -   b. the digital breast model can be generated from the data        obtained from the imaging modalities or by processing one or        multiple individual images using techniques such as image        filtering, edge detection, segmentation, intensity        transformation, multiview reconstruction, photogrametry,        marching cubes, marching tetrahedrons or any combination of        these methods, or any other method that results in a 3D        representation of the breast. If desired, the digital breast        model can include the internal breast structure such as lobules,        blood vessels and milk ducts, where available. The resulting        breast model can be used in its current state or modified to        remove or add texture features either using a Computer Aided        Design (CAD) software, a modeling software, or any software in        which the model can me modified or smoothed to include new        features;    -   c. collected clinical images are formed into a 3D model through        image analysis;        -   one of the embodiments of image combination includes the            following,            -   1. remove artifacts in MRI images,            -   2. segment the breast in the MRI images,            -   3. images are stacked and a model is formed, and            -   4. model is smoothed to create seamless digital model of                actual breast shape.

In accordance with an aspect of the present disclosure, there isprovided a procedure to generate thermal images using a digital breastmodel:

-   -   a. using digital model generated from clinical images;    -   b. a mesh to divide the computational domain. The mesh can be        generated by any software or procedure. It is desired that the        minimum number of mesh elements is 1000 for volumetric meshes        and 100 for surface meshes. A better resolution can be achieved        using at least 100000 elements for volumetric and at least 5000        for surface meshes. The quality of the elements in the resulting        mesh must be within recommended values for accurate numerical        computations. For example, the skewness of the mesh elements        must be below 0.95, with preferred values below 0.7. Depending        on the sophistication of the software used, the actual number of        mesh elements can be smaller or larger;    -   c. defining the governing equation for heat transfer in tissues.        The governing equations can be analytical, empirical,        semi-empirical or any combination and any number of these. Some        examples are the Pennes Bioheat Equation, the Countercurrent,        and the Jiji models. These governing equations may or may not        take into account the effect of the vasculature on the        temperature calculations. This effect can be included either        using models for the vasculature, from clinical images or        artificially generated using software such as Vascusynth;    -   d. defining values of properties of the tissue and other thermal        and biological factors; and    -   e. defining boundary conditions. The surface of the breast is        generally exposed to the still air, for which a convective        boundary condition can be used. The value of the heat transfer        coefficient considering a mixture of radiation, natural        convection and evaporation from the skin is typically in the        range of 5-25 W/m²-K; however any value can be used. Other        alternative is to include forced convection or natural        convection by modeling the surroundings of the breast, a fixed        initial surface temperature, radiation effects or any        combination of these to account for the heat transfer between        the model and its surroundings. For the other surfaces of the        domain, any relevant boundary condition can be used, including        but not limited to, fixed temperature, known heat flux, known        temperature distribution, temperature distribution from        experimental or analytical data, symmetry, insulated faces.        These conditions can be either stationary or time dependent.

In accordance with an aspect of the present disclosure, there isprovided a procedure to utilize matching algorithm to determine tumorcharacteristics:

-   -   a. isolating the breast and obtain thermal images using an        infrared camera;    -   b. generating digital breast model;    -   c. inputting tumor parameters, including but not limited to        size, location, shape, aspect ratio, metabolic activity, blood        perfusion. These values can be obtained from clinical images,        patient data, or can be guessed as an initial value in an        iterative procedure;    -   d. generating thermal images of the breast surface using,        -   i. a digital model and initial tumor characteristics,        -   ii. thermal simulation software, alternatively, artificial            intelligence algorithms including but not limited to neural            networks and support vector machines can be used;    -   e. selecting the region to be analyzed in the images and compute        thermal identifiers. The thermal identifiers may include        information on the tumor properties and how they affect the        surface temperature profile. The thermal identifiers are used to        characterize the thermal distribution and include but are not        limited to information on maximum temperatures, minimum        temperatures and the gradient throughout the tissue. The region        to be analyzed can be further divided into subdivisions;    -   f. comparing the infrared and computed thermal images using a        cost function including but not limited to error, mean squared        error, correlation, cross-entropy. The cost function is any        mathematical function to measure discrepancies between the IR        and computed thermal images;    -   g. updating the value of the tumor characteristics in an        iterative procedure using optimization methods including but not        limited to, Levenberg-Marquardt, Gradient Descent, Newton,        Steepest Descent, Particle Swarm Optimization, Simulated        Annealing Method, Genetic Algorithms, Sequential Quadratic        Programming;    -   h. continuing the method until cost function is below a        predetermined threshold; and    -   i. identifying the estimated tumor characteristics as the        outcome from the algorithm.

In accordance with an aspect of the present disclosure, there isprovided a procedure to utilize wireless technology for remote, portableinfrared imaging to identify suspected malignancy:

-   -   a. portable infrared imaging camera is used in specific        orientations including but not limited to frontal, oblique        views, downward looking, upward looking on the body part being        imaged;    -   b. transmitting these images to a processing center for further        evaluation;    -   c. images are processed using techniques described in Method        A—Method D to identify suspected malignancy;    -   d. further actions will be taken for further evaluation        including,        -   i. doctors consulted, and        -   ii. further imaging including but not limited to x-ray,            mammography, IRI detection, ultrasound, MRI, CT scan,            physical examination, etc.

In accordance with an aspect of the present disclosure, there isprovided a procedure to monitor the usage and efficacy of chemotherapyand/or radiation for progression of treatment:

-   -   a. chemotherapy drugs or radiation is induced into the growing        tumor to shrink the mass;    -   b. cancer patients receive periodic infrared screening to        observe the tumor's activity including,        -   i. as treatment is given, the tumor should begin to shrink            and its metabolic activity should reduce,        -   ii. if shrinkage not observed, treatment may be ineffective;            and    -   c. discuss outcome with consulting physician and alter treatment        accordingly.

The disclosure will be further illustrated with reference to thefollowing specific examples. It is understood that these examples aregiven by way of illustration and are not meant to limit the disclosureor the claims to follow.

Example 1, Method A

One example of varying gridline patterns can be seen in FIG. 10. In FIG.10A the grid is composed by latitudinal lines (parallels) andlongitudinal lines (meridians) that match the contours of the breast;this grid is similar to the coordinate system used to locate points onthe Earth surface. The grid shown in FIG. 10B is composed of horizontaland vertical, similar to a Cartesian coordinate system. The grid canalso be composed of oblique lines, circular segments, hyperbolic,parabolic lines, functions resulting from statistical fitting(quadratic, cubic, exponential, logarithmic, linear, etc.), or anycombination of these. They can be matched on the breast outline. Thepoints where the two types of lines intersect define the nodes of thegrid. These nodes (for example A, B, C and D in FIG. 10A or A′, B′, C′and D′ in FIG. 10B) define the regions (R1 or R1′) where the quantitiesof interest will be obtained.

Example 2, Method A

Another example gridding system, seen in FIG. 11, can show theimportance of a finer mesh. The hot spot resulting from the tumor is onthe left side of the breast and the hot spot resulting from a vein is onthe right side of the breast. The grid system is drawn over the breastsurface with horizontal (latitude) and vertical (longitude) lines. Whena region of interest is identified, a finer meshing system is applied.The average temperature value in each square can be found to determinethe significance of the hot spot. With a finer grid in specified regionsof interest, the average temperature values will change. Thetemperatures at the hot spot will be significantly hotter than thesurrounding tissue. Using a grid system can help identify potentialregions of concern as well as differentiate between a tumor and a vein.

Example 3, Method A

As an example, the mean (Tmean), maximum (Tmax) and minimum (Tmin)temperatures and standard deviation (SD) were computed in the regionsshown in FIG. 12A. The statistical parameters are shown only in sixregions to illustrate the advantages of using the thermal grid. In orderto conduct a complete analysis, all regions should be analyzed. Inaddition to such statistical parameters, other indicators includeentropy, energy, histogram analysis, texture, variance, correlation,contrast, skew, kurtosis and any combination or product of these. FIG.12B shows the values of these parameters. Region 1 is expected to have ahigh mean temperature relative to other regions; besides the temperaturerange is only 0.5° C. Region 2 is expected to have a mean temperaturelower than Region 1; however, the mean temperature is 1.7° C. higherthan Region 1. The temperature range in Region 2 is 2.7° C. and thestandard deviation is high, which indicates that a possible abnormalityis found near Region 2. Region 3 also presents an elevated temperaturewith a range of 1° C. Regions 4 and 5 present lower and more uniformtemperatures than Regions 2 and 3. From the previous analysis, thepossible abnormality is located in the vicinity of Regions 2 and 3. Thedensity of lines can be increased in such Regions to aid in theidentification of abnormal temperatures. With fewer grid boxes, it willbe easier to find the boundaries of the thermal abnormality in order todetermine if malignancy is a concern or if stray vasculature is creatingadditional heat signatures.

Although an example of the grid pattern is presented above, any othertype of grid pattern, and temperature calculations based on thetemperature of the nodes, several pixels around nodes, or differentregions identified by the grid patterns can be used to arrive at thethermal markers. The temperature elevation in a region over anunaffected region may be 3° C. or higher for aggressive tumor, or tumorclose to the surface, 1-3° C. for the smaller tumor or deep inside, orbetween 0.1 to 1° C. for very small or very deep tumors. The small andlarge are somewhat qualitative, generally small meaning less than 7 mm,medium being between 0.7-2 cm and large being greater than 2 cm. Theseboundaries are not fixed and may be changed depending on the individualcase depending on the breast size. Tumor depths are also somewhatsubjective and may be indicative of near surface, around within 10 mm,or moderate around 10 mm to 20 mm, and deep beyond these values. Theseare also subject to variation depending on tumor location, breast size,etc., similar to other classification presented earlier and vice versa.

Example 4, Method A

FIG. 13 shows the breast thermogram of an individual with breast cancer.Lines 1 and 2 are latitudinal lines, Lines 3 and 4 are longitudinallines. FIG. 14A shows the temperature profile for Lines 1 and 2, thefiltered profiles for these lines are shown in FIG. 14B. The temperatureof Line 1 starts from ˜28° C. near the chest wall and decreases. After 1cm, the profile flattens and shows a peak at around 5 cm; thetemperature rise is ˜0.5° C. with respect to the surroundingtemperatures, which helps locate possible malignancy. The profile ofLine 2 decreases from its maximum near the chest wall to its minimum atthe tip of the breast. This profile does indicate that no abnormality isobserved along Line 2. FIG. 14C shows the temperature profile for Lines3 and 4, the filtered profiles for these lines are shown in FIG. 14D.The temperature along Line 3 increases continually and then decreasesnear 3 cm. This change in slope (temperature gradient) indicates thepresence of an abnormality. The temperature of Line 4 is almost uniformwith only slight variations, which indicates that no abnormalities areobserved along Line 4. In the previous example, the temperature of Line1 starts from ˜28° C. near the chest wall and decreases. After 1 cm, theprofile flattens and shows a peak at around 5 cm. The temperature riseis ˜0.5° C. with respect to the surrounding temperatures, whichindicates possible malignancy. The profile of Line 2 decreases from itsmaximum near the chest wall to its minimum at the tip of the breast.This profile indicates that no abnormality is observed along Line 4.

Example 5, Method A

Both tumors and blood vessels cause local temperature rises. The aspectratio D1/D2, where L1, the largest dimension, is close to one whichindicates that the abnormality is likely caused by a tumor. The aspectratio for possible tumors can be from 1 to 4. This value may be largerif the tumor is large, which will show up as wider regions in the normaldirection, while the blood vessels will not generally be wider thanabout 5 mm or 7 mm. The aspect ratio d1/d2 is ˜15, which indicates thatthis is likely caused by a blood vessel. Aspect ratios larger than five(5) are indicative of blood vessels. In some cases, these values may befurther refined based on the actual width of the enhanced thermal regionsince tumors would be wider while the blood vessels would be narrower.This needs to be further evaluated with consideration of DCIS which maypresent a somewhat similar to the effects of blood vessels. Theseresults correlate with the tumor location obtained from the MRI imagesand the surface blood vessels from the MRI rendering shown in FIG. 15for Subject 6.

FIG. 16 shows temperature contours for the same subject. Thesetemperature contours are warmer (red), more circular and closed in theregion surrounding the tumor. In the region surrounding the blood vesselof interest, the contours have higher aspect ratios and are colder thanfor the region surrounding the tumor, which shows the potential oftemperature contours to distinguish between possible tumors and bloodvessels from infrared images. Thermal contours are also effectivethermal markers of the cancer. If the contour plots show concentricregions that are indicative of steep hill type feature, the region maybe a suspect. If the gradient in this region is high as discussedearlier, then it can be used as a further marker.

Example 6, Method B

A digital breast model was generated from MRI images of the breast andit was used to generate and validate temperature distributions withclinical images. A succinct flowchart of the digital model process isseen in FIG. 17.

The MRI study, consisted of 178 images, the region containing the breastof interest was selected. Then, the tumor was measured and its locationwas stored for future steps. The tumor was modeled as a sphere with adiameter of 2.7 cm. A 3D median filter was applied, the dimensions ofthe applied 3D median filter were (3, 3, 3). The outline of the breastwas detected using a modified version of the Canny edge detector, whichdetected a continuous outline of the breast. Then, the breast wassegmented by defining everything inside the breast outline as breasttissue and the region outside as the background. The breast surface wasgenerated from the stack of MRI slices using the Marching Cubesalgorithm, which results in a surface mesh composed of triangularelements. The resulting surface mesh was jagged and needed to besmoothed to represent more accurately the geometry of the breast. Thesurface mesh was smoothed using an algorithm that replaced the angle ofa mesh face with the average angle of the neighboring faces; which issimilar to applying an averaging filter to a 3D image. In the smoothedbreast geometry, some regions of the mesh were further smoothed usingthe software Autodesk Recap Photo only on the regions that needed it.The resulting surface mesh is seamless and accurate.

The generated digital breast model is shown in FIG. 18. This model wasused to compute the surface temperature.

The commercial software ANSYS Fluent was used to predict the breastsurface temperature profile. The solution is based on solving theunderlying heat transfer equations with convective blood flow. ThePennes' bioheat equation was used to account for the thermalinteractions occurring within the breasts and in the environment.Pennes' equation is given by:

$\begin{matrix}{{\rho_{t}{c_{t}\left( \frac{\partial T_{t}}{\partial t} \right)}} = {{\nabla{\cdot \left( {k_{t}{\nabla T_{t}}} \right)}} + {\omega_{b}{c_{b}\left( {T_{a} - T_{t}} \right)}} + q_{m}}} & (1)\end{matrix}$

where ρ, c and k are the density, specific heat and thermalconductivity, respectively. The subscripts t, b and a refer to tissue,blood and arteries, respectively, ω is the blood flow rate per unittissue volume (perfusion rate in kg/m³-s) and q_(m) is the metabolicactivity within the tissue in W/m³. The computational domain used tocompute the temperature distribution is shown in FIG. 19. The breastsurfaces are subjected to a convection boundary condition, where valuesof the ambient temperature and heat transfer coefficient can bereasonably well estimated and entered in the software. The chest wall isconsidered to be at the core temperature of the body.

To take into consideration the two blood perfusion and metabolicactivity terms in the Pennes equation, these terms were defined assource terms in the software using User Defined Functions (UDFs). TheUDFs were prepared to vary the location and size of the tumor withoutneed to again mesh the tumor domain separately. This offers flexibilityif the position and size of the tumor is changed in the model becausethere is no need to re-mesh the domain; only the UDF will be modified toaccount for the new tumor position and size.

TABLE 1 Values of the parameters used to compute the thermal images.Parameter Value Unit Thermal conductivity (k) 0.42 W m⁻¹ K⁻¹ Perfusionrate of healthy tissue (ω_(h)) 1.8 × 10⁻⁴ s⁻¹ Perfusion rate of tumor(ω_(t))   9 × 10⁻³ s⁻¹ Metabolic activity of healthy tissue (q_(h)) 450W m⁻³ Metabolic activity of tumor (q_(t)) 6350 W m⁻³ Temperature ofarteries (T_(a)) 37 ° C. Specific heat of blood (C_(b)) 3,840 J kg⁻¹ K⁻¹Density of blood (ρ_(b)) 1,060 Kg m⁻³ Core temperature (T_(c)) 37 ° C.Ambient temperature (T_(∞)) 21 ° C. Heat transfer coefficient 10.5 W m⁻²K⁻¹

The metabolic activity of the tumor was obtained using the formuladeveloped by Pennes, where dt is the tumor diameter:

$\begin{matrix}{q_{t} = \frac{3.27 \times 10^{6}}{{468.5\mspace{11mu} {\ln \left( {100\; d_{t}} \right)}} + 50}} & (2)\end{matrix}$

Where q_(t) is the metabolic activity in W/m3 and d_(t)=0.027 m (2.7 cm)is the tumor diameter. Using (2) results in a value of the metabolicactivity of 6,350 W/m³. Using the parameters listed in Table 1, thethermal images were obtained. FIG. 20 shows the comparison of theclinical infrared images and the computed thermal images for threedifferent positions. The digital model computed accurately thetemperature distribution and predicted the thermal trends observed inthe clinical infrared images.

The median temperature in each of the regions was computed, whichallowed to filter the effect of small blood vessels. The absolute errorbetween the clinical and computed temperature distributions was computedusing:

$\begin{matrix}{E = {\sum\limits_{1}^{n_{r} \times n_{c}}\; {{T_{\exp,i} - T_{{num},i}}}}} & (3)\end{matrix}$

Where Texp and Tnum are the clinical and numerical temperature vectors,respectively, which contain all the individual temperature values ofeach of the individual regions. Table 2 lists the values of E, as wellas the value of mean absolute error for each region. The mean absoluteerror per individual region is 0.12, which indicated that the model isaccurately captures the temperature distribution observed in clinicalimages; therefore, the modeling approach is validated and can be usedconfidently to compute the temperature distribution for additionalcases.

TABLE 2 Absolute error between clinical and computed thermal images. E[° C.] E/n_(r) × n_(c) [° C./region] 17.14 0.12

Example 7, Method C

The method described was used to estimate simultaneously five parametersnamed thermal conductivity of healthy tissue k_(h), tumor size(diameter) d, tumor position within the breast (x_(t), y_(t), z_(t)) ina breast with cancer.

The parameters were estimated using a digital breast model (d) generatedfrom a sequence of MRI images (c). A set of initial parameters (a) wasused us current value of the parameters (b) to numerically solve (e) thegoverning equations to generate surface temperature profiles (f). Thegenerated profiles were processed in Matlab (g, j). The temperature ofvarious regions was extracted in both the numerical and target images(h, k). Both temperatures were compared using the Levenberg-Marquardtalgorithm (l), if the convergence criteria (m) is not met, theparameters are updated (o) and the process is repeated. If theconvergence criteria is met, the current value of the parameters isaccepted as the estimated value of the parameters of interest. Thisprocess is outlined in FIG. 25.

A digital model of the female breast in prone position was generatedfrom sequential Magnetic Resonance Imaging (MRI) images. The MRI imageswere individually filtered to reduce noise. Then, the outline of thebreast was identified and the breast segmented in every slice. Thesequential segmented images were stacked, which resulted in a digitalbreast model. This model was smoothed to generate a seamless andcontinuous breast model. The digital breast model was generated from theright breast of a 68-years-old woman with a grade III tumor and adiagnosis of invasive ductal carcinoma. The tumor was located at 12o'clock, 2 cm from the nipple. The tumor volume was measured; althoughits shape is irregular, its volume was used to model a spherical tumorwith a diameter of 2.7 cm and equivalent volume. Using Eq. (2) resultsin a tumor metabolic activity of Q_(t)=6350 W/m³.

The Pennes' bioheat equation (1) was used to account for thermalinteractions within the breast and with the environment.

Pennes bioheat equation is subject to the following boundary conditions(FIG. 24): convection between the surface of the breast and theenvironment at Face F (4), constant temperature at the chest walltemperature T_(c) at Face E (5), no heat flux across Faces 1, 2, 3 and 4(6). In our computational model, we consider only two different tissues,gland (healthy) and tumor.

$\begin{matrix}{{{{- k}\frac{\partial T}{\partial n}}_{{at}\mspace{11mu} F}} = {h\left( {T_{s} - T_{\infty}} \right)}} & (4) \\{{T_{{at}\mspace{11mu} E}} = T_{c}} & (5) \\{{\frac{\partial T}{\partial n}_{{{at}\mspace{11mu} A},B,C,D}} = 0} & (6)\end{matrix}$

The software ANSYS Fluent was used to numerically solve Pennes bioheatequation (1) in the digital breast model. This software uses the finitevolume method to discretize the governing equation and provide anumerical solution for the problem. The perfusion and metabolicgeneration terms in (1) were introduced as source terms through UserDefined Functions (UDFs). The UDFs allow to vary the tumor position andsize without need to recalculate the mesh in the computational domaingiven a proper mesh; a mesh with 3.5 million elements was created forthis purpose. The value of the constant parameters used to simulate thetemperature distribution is shown in Table 3.

TABLE 3 Value of the constant parameters used in the simulations.Property Value Unit Perfusion rate of healthy tissue (ω_(h)) 1.8 × 10⁻⁴s⁻¹ Perfusion rate of tumor (ω_(t))   9 × 10⁻³ s⁻¹ Metabolic activity ofhealthy tissue (Q_(h)) 450 W m⁻³ Temperature of arteries (T_(a)) 37 ° C.Specific heat of blood (C_(b)) 3,840 J kg⁻¹ K⁻¹ Density of blood (ρ_(b))1,060 kg m⁻³ Core temperature (T_(c)) 37 ° C. Ambient temperature(T_(∞)) 21 ° C. Heat transfer coefficient (h) 13.5 W m⁻² K⁻¹

A text file containing initial values of the five parameters wasprepared to start the process of parameter estimation. The initialvalues were positive and in the range of values shown in Table 4. Therange of values for the thermal conductivity was obtained from datareported in the literature; the minimum is for a completely fatty breastand the maximum is for an extremely dense breast. In the case of tumordiameter, the minimum value (9.9 mm) results in the maximum metabolicactivity reported by Gautherie, the maximum is a 7 cm tumor, which wouldbe easily palpable. For the case of tumor location, its center must liewithin the computational domain.

TABLE 4 Range of values for the parameters to estimate. ParameterMinimum Value Maximum Value Units k_(h) 0.15 0.8 W m⁻¹K⁻¹ d 0.0099 0.07m x_(t) 0.06 0.16 m y_(t) 0.08 0.16 m z_(t) 0.08 0.15 m

The surface temperature for each set of parameters was obtained throughnumerical simulations using the generated digital breast model. Thetechnique was tested by estimating the parameter values from the targetimages using the technique described. In order to obtain the temperaturealong the entire breast surface, eight different views around the breastmodel were generated, each separated 45° clockwise in the XZ plane andoriented at 25° with the Y axis. The surface temperature distribution onthe eight different views was exported as an image from ANSYS Fluent.The resulting images were read in MATLAB®. First, the region of thebreast was isolated and the image intensity values were transformed totemperature values using an in house code in each of the eight images.Only the central part of each breast image was analyzed to avoidanalyzing the same region in more than one image. Then, a rectangularregion of interest (ROI) was defined in each of the images as shown inFIG. 25. The ROI in each image was divided into 12 rows by 6 columns.Resulting in n_(r)×n_(c) sub regions, R_(i), in each of the eightimages. The mean temperature of the pixels in each of the sub regionswas computed and used to represent the temperature of each sub region.An arbitrary location was selected as the origin and all breast andtumor coordinates were adjusted accordingly. The initial tumor locationwas placed inside the central region of the breast.

We used the Levenberg-Marquardt algorithm to estimate the set ofparameters β defined as:

β=[k _(h) d x _(t) y _(t) z _(t)]^(T)  (7)

the algorithm is used to minimize the objective function defined by:

S(β)=[T _(exp) −T(β)]^(T)[T _(exp) −T(β)]  (8)

The objective function (11) is the mean squared error between theexperimental (target) temperature T_(exp), and the current estimatedtemperature distribution T(β). The algorithm uses a damping parameter,μ, which value changes at every iteration and had an initial value of0.001. A central difference scheme was used to compute each element ofthe Jacobian matrix, matrix formed by the derivatives with respect tothe parameters,

$\begin{matrix}{\frac{\partial{T_{i}(\beta)}}{\partial\beta_{j}} \approx \frac{{T_{i}\left( {\beta_{j} + {ɛ\beta}_{j}} \right)} - {T_{i}\left( {\beta_{j} - {ɛ\beta}_{j}} \right)}}{2{ɛ\beta}_{j}}} & (9)\end{matrix}$

Once the Jacobian matrix is computed, the parameters are updatedaccording with the equation:

β^(k+1)=β^(k)+[(J ^(k))^(T) J ^(k)+μ^(k)Ω^(k)]⁻¹(J ^(k))^(T)[T _(exp)−T(β^(k))]  (10)

Where the subscript k refers to the current value of parameters, and thesubscript k+1 refers to the updated value of parameters. The matrix Ω isdiagonal and its aim is to damp oscillations and instabilities by makingits components large as compared to those of (J^(k))^(T) J^(k) ifneeded. The matrix Ω is defined as:

Ω=diag(J ^(T) J)  (11)

The algorithm used stopped stops when at least one of three conditionswere met. In the first condition (12), the algorithm runs for a maximumof k_(max) iterations, where k_(max)=50 is used. In the second condition(13), the algorithm stops and accepts the current parameters as the bestestimations when the objective function is lower than a small value ε₁,where ε₁=10⁻³. The last condition (14), implies that the algorithm stopswhen the norm of the difference between current and updated parametersis lower than a value ε₂, where ε₂=10⁻¹⁰. In this case, the updatedparameters are accepted as the best estimations of the actual values.

k>k _(max)  (12)

S(β^(k+1))<ε₁  (13)

∥β^(k+1)−β^(k)∥<ε₂  (14)

Using volumetric images of the breast with tumor helps to visualize thelocation of the tumor within the breast. FIG. 27 shows the actual tumorwithin the breast, the estimated tumor and the image registrationbetween actual and estimated, where it is observed that the estimationsmatch closely the actual location and size. The volumetric images withthe estimated tumor can be useful in a clinical to aid in the locationof the tumor for biopsy.

Example 8, Method D

The geometrical parameters that can be used include but are not limitedto width (W), height (H), length (L), as seen from the images and anycombination or function of these. A minimum of two images obtained fromdifferent views are necessary to categorize the breast. It is preferredto use images looking at the breast from the head (axial) and imageslooking at the breast from the side (sagittal) but any combination andnumber of images in any orientation can be used. The geometricparameters are measured from the base of the breast, which is a planeparallel to a Coronal plane. The base of the breast is identified as aplane parallel to the imaging table from the surface of the chestcontacting the imaging table that can be located at any distance fromthe imaging table, one example could be that the base plane is coplanarto the bottom face of the imaging table. A rectangle surrounding thebreast can be defined in any of the views to help in defining furtherindicators of the breast shape. The area defined by each of therectangles is the maximum area that a breast with base and H dimensionscan occupy in a scene. By computing the ratio between the actual area ofthe breast to the area of the rectangle, a fullness indicator isobtained. The fullness indicator, together with the other geometricidentifiers are used to geometrically classify the breasts. An exampleof how to obtain the geometric identifiers from breast images is usingFIG. 28.

From an axial view of the breast the following parameters can be definedusing equations (15) and (16). From a sagittal view of the breast thefollowing parameters can be defined using equations (17) and (18).Combining the information from the two views will define equation (19).

$\begin{matrix}{{AAR} = \frac{W}{H}} & (15) \\{{AF} = \frac{{Breast}\mspace{14mu} {area}\mspace{14mu} {in}\mspace{14mu} {view}}{WH}} & (16) \\{{SAR} = \frac{L}{H}} & (17) \\{{AF} = \frac{{Breast}\mspace{14mu} {area}\mspace{14mu} {in}\mspace{14mu} {view}}{LH}} & (18) \\{{CAR} = \frac{W}{L}} & (19)\end{matrix}$

Any combination, function or power of these parameters can be usedinstead of the previously described. Other parameters that can be usedto describe the breast shape and size is by mapping, fitting or matchingthe breast outline in any of the views to any algebraic, trigonometric,exponential or logarithmic function and provide the relevant parametersfound.

Although various embodiments have been depicted and described in detailherein, it will be apparent to those skilled in the relevant art thatvarious modifications, additions, substitutions, and the like can bemade without departing from the spirit of the disclosure and these aretherefore considered to be within the scope of the disclosure as definedin the claims which follow.

What is claimed:
 1. A method for identifying suspected malignancy withintissue using thermal imaging comprising: constructing a geometric map ofthe surface of a body part; generating thermal indicators from a thermalinfrared image of the body part surface; identifying a region ofinterest from the thermal indicators; and identifying suspectedmalignancy within the region of interest.
 2. The method of claim 1,wherein constructing a geometrical map comprises drawing gridlines onthe body part image, drawing lines through regions of interest, ordrawing contours around regions of interest.
 3. The method of claim 1,wherein the thermal indicators are generated from gridlines by findingtemperature averages, lines drawn through the region of interest byanalyzing the temperature changes over a specified distance or contoursby observing the dimensions of the region of interest.
 4. The method ofclaim 1, wherein the thermal indicators classified as the temperaturedifference between maximum and minimum temperatures on a surfaceinfrared image are identified as higher than 0.2° C. within a range of 2mm to 10 mm, are considered abnormal and potentially suspicious withinthe body part.
 5. The method of claim 1, wherein identifying thesuspected malignancy comprises classifying the temperature differencebetween maximum and minimum temperatures higher than 0.5° C. over a 5 mmto 40 mm range as a suspected tumor and classifying the temperaturedifference between maximum and minimum temperatures between 0.2° C. and2.0° C. over a 1 mm to 5 mm range as a suspected blood vessel.
 6. Amethod for localizing suspected malignancy within a tissue of a bodypart, comprising: generating a 3D digital model of the body part frommultiple images, including phantom thermal images; obtaining multiplesurface infrared images of the body part; comparing the phantom thermalimages with the respective surface infrared images and refining thecompared phantom thermal images; and localizing a suspected malignancywithin the body part.
 7. The method of claim 6, wherein the multiplesurface infrared images are obtained from multiple views covering theentire body part.
 8. The method of claim 6, wherein the 3D digital modelof the body part comprises internal structure generated from infrared,ultrasound, magnetic resonance, or x-rays by processing multi-viewreconstruction, image stacking or photogrammetry.
 9. The method of claim6, wherein the phantom thermal images are generated through thermalsimulation software.
 10. The method of claim 6, wherein the localizationof the suspected malignancy is conducted using an initial guess anditeratively improving a match between the surface infrared images andthe phantom thermal images until a convergence criteria defined by theuser is met.
 11. The method of claim 6, wherein the surface infraredimages and the phantom thermal images are aligned using imageregistration in order to compare the surface infrared images and thegenerated phantom thermal images.
 12. A method for determining suspectedmalignancy within tissue, comprising: generating a digital library ofclinical images of body parts including thermal and geometric data ofthe body parts; identifying geometric and thermal identifiers of thevarious body parts; comparing clinical images to other images withsimilar geometric identifiers; comparing thermal identifiers ingeometrically similar clinical images; and identifying suspectedmalignancy in the clinical images.
 13. The method of claim 12, furthercomprising updating the digital library with clinical data, thermalidentifiers and geometric identifiers of body parts.
 14. The method ofclaim 12, wherein the geometric identifiers comprise the body partshape, body part size, body part circumference, specific distancesbetween identifiable regions within the body part or volume.
 15. Themethod of claim 12, wherein the thermal identifiers comprise maximumtemperatures, minimum temperatures, or temperature changes throughoutthe tissue.